NDLI: Uncertainty Analysis of Greenhouse Gas (GHG) Emissions Simulated by the Parametric Monte Carlo Simulation and Nonparametric Bootstrap Method

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Uncertainty Analysis of Greenhouse Gas (GHG) Emissions Simulated by the Parametric Monte Carlo Simulation and Nonparametric Bootstrap Method

Content Provider	MDPI
Author	Lee, Kun Mo Lee, Min Hyeok Lee, Jong Seok Lee, Joo Young
Copyright Year	2020
Description	Uncertainty of greenhouse gas (GHG) emissions was analyzed using the parametric Monte Carlo simulation (MCS) method and the non-parametric bootstrap method. There was a certain number of observations required of a dataset before GHG emissions reached an asymptotic value. Treating a coefficient (i.e., GHG emission factor) as a random variable did not alter the mean; however, it yielded higher uncertainty of GHG emissions compared to the case when treating a coefficient constant. The non-parametric bootstrap method reduces the variance of GHG. A mathematical model for estimating GHG emissions should treat the GHG emission factor as a random variable. When the estimated probability density function (PDF) of the original dataset is incorrect, the nonparametric bootstrap method, not the parametric MCS method, should be the method of choice for the uncertainty analysis of GHG emissions.
Starting Page	4965
e-ISSN	19961073
DOI	10.3390/en13184965
Journal	Energies
Issue Number	18
Volume Number	13
Language	English
Publisher	MDPI
Publisher Date	2020-09-22
Access Restriction	Open
Subject Keyword	Energies Environmental Engineering Uncertainty Analysis Ghg Emission Factor Parametric Monte Carlo Simulation Nonparametric Bootstrap R Program
Content Type	Text
Resource Type	Article

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